The present application relates generally to an improved data processing apparatus and method and more specifically to mechanisms for ranking related objects using blink model based relation strength determinations.
Many real-world applications involve determining relations between two or more objects. These applications utilize metrics for evaluating the “relatedness” of one object to one or more other objects. These metrics are sometimes referred to as relation-strength metrics, distance metrics, similarity metrics, ranking scores, or the like. Examples of such applications include search engine recommendations which recommend web pages that are related to a web page being viewed by a user based on the inter-linking graph between web pages. Other examples include social media websites that provide recommendations of contacts of individuals, e.g., recommending potential new contacts to a user based on an existing social network graph. Still further examples include web sites that recommend multi-media content based on recently viewed multi-media content and commonality of attributes among the multi-media content expressed in a graph. In yet another example, web sites that recommend products based on the item being viewed by a user and similarity between products. Many other examples may be provided in which the application is determining relationships between objects for purposes of performing a processing operation and generating an output.
There are many solutions used by such applications for determining the metrics indicative of relatedness. Examples of these solutions include the PageRank algorithm, SimRank algorithm, Adamic/Aday, and Katz algorithms. The drawback of these solutions is that they have relatively low accuracy. For example, in Liben-Nowell and Kleinberg, “The Link-Prediction Problem for Social Networks,” Journal of the American Society of Information Science and Technology, 2007 it was demonstrated that, for link prediction for co-authorship social networks, the best accuracy that was able to be achieved was only 16% accuracy.